Lab 1: Wrangling review and a few new things

1 ESM 244 Lab-1

1.1 Attach Packages

library(tidyverse)
library(here)
library(sf)
library(tmap)

1.2 Read in the data

sfo_trees_df <- read.csv(here("data", "sfo_trees", "sfo_trees.csv")) %>% 
  janitor::clean_names()

1.3 Part 1: Data Exploration and Wrangling Review

1.3.1 Example 1

1. Find counts of observations by legal_status. Then select the statuses with the five highest tree counts.

top_5_status <- sfo_trees_df %>% 
  filter(!is.na(legal_status)) %>% 
  ### drop_na(legal_status) %>% ### same thing!
  group_by(legal_status) %>% 
  summarise(tree_count = n()) %>% 
  ungroup() %>% 
  slice_max(tree_count, n = 5) %>% 
  arrange(-tree_count)

2. Make a graph of top 5 from above.

top_5_plot <- ggplot(top_5_status, aes(x = legal_status, y = tree_count)) +
  geom_col() +
  labs(
    y = "Tree Count",
    x = "Legal Status",
    title = "Top 5 legal status of SFO trees"
  ) +
  theme_bw()

top_5_plot

Figure 1: SFO Trees Top 5 Legal Status

Results are in figure 1 Figure 1

1.3.2 Example 2

1. Only keep observations where legal status is Permitted Site and caretaker is MTA or DPW. Store as permitted_mta_dpw.

permitted_mta_dpw <- sfo_trees_df %>% 
  filter(legal_status == "Permitted Site" & caretaker %in% c("MTA", "DPW"))

1.3.3 Example 3

1. Only keep observations of oak and pine trees, then only keep columns species, legal_status, plant_date, latitude, and longitude. Note which category each tree falls into, as column type. Store as oak_pine_df.

oak_pine_df <- sfo_trees_df %>% 
  filter(str_detect(species, "Oak") | str_detect(species, "Pine")) %>% 
  select(species, legal_status, plant_date, latitude, longitude) %>% 
  mutate(
    type = ifelse(str_detect(species, "Oak"), "Oak", "Pine")
  )

2. Make a little graph of locations. Which trees are oak vs pines?

oak_pine_plot <- ggplot(oak_pine_df, aes(y = latitude, x = longitude, colour = type)) +
  geom_point() +
  theme_bw() +
  theme(axis.title = element_blank()) +
  labs(
    color = "Tree Type",
    title = " Locations of Oak and Pine Trees in SF"
  )

oak_pine_plot

1.3.4 Example 4

Load a list of CA native species.

ca_native_df <- read_csv(here("data", "sfo_trees", "ca_native_spp.csv"))

How can we compare the California native species to those in our SF trees data? Add a column notes whether each tree is a CA native or not, and save as sfo_trees_native (include species info, legal status, plant date, and location). Then, count how many native vs. non-native trees for each legal status category, and save as sfo_native_status. Extension: include how many individual species in each category as well!

# ca_native_df <- ca_native_df %>% 
#  mutate(species = paste(scientific_name, " :: ", str_to_title(common_name)))

# sfo_trees_native <- sfo_trees_df %>% 
#  select(species, legal_status, plant_date, address, latitude, longitude) %>% 
#  full_join(ca_native_df)

sfo_trees_native <- sfo_trees_df %>% 
  separate(species, into = c("spp_sci", "spp_common"), sep = " :: ") %>%
  select(starts_with('spp'), 'plant_date', 'legal_status', 'longitude', 'latitude') %>%
  mutate(ca_native = (spp_sci %in% ca_native_df$scientific_name))

sfo_native_status <- sfo_trees_native %>%
  group_by(legal_status, ca_native) %>%
  summarize(n_trees = n(),
            n_species = n_distinct(spp_sci))

1.4 Part 2: Analysis and Quickie Maps

Considering only Coast Live Oak and Monterey Pine, have tree planting preferences changed over time?

1.4.1 Wrangling

Create a new dataframe that contains only Coast Live Oak and Monterey Pine observations (NOT all oaks and pines!), and include information on year and location. Call this oak_pine_year_df.

Then, determine whether there is a difference in when trees have been planted.

oak_pine_year_df <- sfo_trees_native %>% 
  filter(spp_sci %in% c("Quercus agrifolia", "Pinus radiata")) %>% 
  mutate(plant_year = year(plant_date))

t.test(plant_year ~ spp_sci, data = oak_pine_year_df)

    Welch Two Sample t-test

data:  plant_year by spp_sci
t = 4.2553, df = 346.94, p-value = 2.69e-05
alternative hypothesis: true difference in means between group Pinus radiata and group Quercus agrifolia is not equal to 0
95 percent confidence interval:
 1.855054 5.043743
sample estimates:
    mean in group Pinus radiata mean in group Quercus agrifolia 
                       2010.552                        2007.102 

ggplot(oak_pine_year_df) +
  geom_histogram(aes(x = plant_year), bins = 10) +
  facet_wrap(~ spp_sci, ncol = 1) +
  theme_minimal()

ggplot(oak_pine_year_df) +
  geom_point(aes(x = longitude, y = latitude, color = plant_year, shape = spp_sci))

oak_pine_sf <- oak_pine_year_df %>% 
  drop_na(longitude, latitude) %>% 
  st_as_sf(coords = c("longitude", "latitude")) # Convert to spatial coordinates

# But we need to set the coordinate reference system (CRS) so it's compatible with the street map of San Francisco we'll use as a "base layer":
st_crs(oak_pine_sf) <- 4326

# Then we can use `geom_sf`!

ggplot(data = oak_pine_sf) +
  geom_sf(aes(color = spp_sci)) +
  theme_minimal()

sfo_map <- read_sf(here("data", "sfo_map", "tl_2017_06075_roads.shp"))

st_transform(sfo_map, 4326)

Simple feature collection with 4087 features and 4 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: -122.5136 ymin: 37.70813 xmax: -122.3496 ymax: 37.83213
Geodetic CRS:  WGS 84
# A tibble: 4,087 × 5
   LINEARID      FULLNAME            RTTYP MTFCC                        geometry
 * <chr>         <chr>               <chr> <chr>                <LINESTRING [°]>
 1 110498938773  Hwy 101 S Off Rmp   M     S1400 (-122.4041 37.74842, -122.404 …
 2 110498937425  Hwy 101 N on Rmp    M     S1400 (-122.4744 37.80691, -122.4746…
 3 1103660229533 Ludlow Aly - No Acc M     S1780 (-122.4596 37.73853, -122.4596…
 4 1106081811301 Mission Bay Blvd N  M     S1400 (-122.3946 37.77082, -122.3929…
 5 1103666896385 25th Ave N          M     S1400 (-122.4858 37.78953, -122.4855…
 6 1103689702566 Willard N           M     S1400 (-122.457 37.77817, -122.457 3…
 7 1103689702762 25th Ave N          M     S1400 (-122.4858 37.78953, -122.4858…
 8 110498933806  Avenue N            M     S1400 (-122.3643 37.81947, -122.3638…
 9 1103689702763 25th Ave N          M     S1400 (-122.4854 37.78983, -122.4858…
10 1103677491290 Mission Bay Blvd S  M     S1400 (-122.3865 37.77086, -122.3878…
# ℹ 4,077 more rows

ggplot(data = sfo_map) +
  geom_sf()

ggplot() +
  geom_sf(data = sfo_map,
          size = 0.1,
          color = "darkgray") +
  geom_sf(data = oak_pine_sf, 
          aes(color = spp_sci),
          size = 0.5) +
  theme_void() +
  labs(title = "Oaks and pines in San Francisco")

tmap_mode("view")

tm_shape(oak_pine_sf) + 
  tm_dots(col = 'spp_sci')